1. Field of the Invention
The present invention relates to method and apparatus for linear predicting, particularly method for linear predicting on received signals in mobile communications and a receiver.
2. Description of the Related Art
In mobile communications, it is a common practice to utilize a least square method as a linear prediction, for correct demodulation of transmission data requires to predict various errors and parameters such as frequency offsets, reference phases, synchronization and radio channels using a limited number of clues such as known signals and provisional judgment signals.
In a conventional least square method, all observed values are equally weighted in the process of linear predicting. This means that both of probable observed values and less probable observed values influenced equally to the result of the linear predicting.
A description of the above conventional least square method is as followed. FIG. 1 is a graph showing the principle of the conventional least square method. This FIG. 1 shows a case of applying six observation points which have regular intervals to the predicting.
FIG. 1 shows observed values 101 to 106, a predicted result 107, Euclidean distances 108 to 113, that is, margins of errors between predicted result 107 and each observed values.
Supposing predicted result 107 and Euclidean distances between observed values 101 to 106 and predicted result 107 as margins of errors 108 to 113, predicted result 107 that will minimize the sum of squares of margins of errors 108 to 113 can be calculated from expressions (1) and (2) below.
Supposing x(i) is an observation point; y(i), an observed value; xe2x80x9cNxe2x80x9d, the number of observation points; and xe2x80x9cAx(i)+Bxe2x80x9d, the linear expression to be obtained, xe2x80x9cAxe2x80x9d and xe2x80x9cBxe2x80x9d are calculated as follows.                     A        =                                            N              ⁢                                                ∑                                      i                    =                    0                                                        N                    -                    1                                                  ⁢                                  {                                                            X                      ⁡                                              (                        i                        )                                                              ⁢                                          Y                      ⁡                                              (                        i                        )                                                                              }                                                      -                                          ∑                                  i                  =                  0                                                  N                  -                  1                                            ⁢                                                X                  ⁡                                      (                    i                    )                                                  ⁢                                                      ∑                                          i                      =                      0                                                              N                      -                      1                                                        ⁢                                      Y                    ⁡                                          (                      i                      )                                                                                                                              N              ⁢                                                ∑                                      i                    =                    0                                                        N                    -                    1                                                  ⁢                                  {                                                            X                      ⁡                                              (                        i                        )                                                              2                                    }                                                      -                                          (                                                      ∑                                          i                      =                      0                                                              N                      -                      1                                                        ⁢                                      X                    ⁡                                          (                      i                      )                                                                      )                            2                                                          (        1        )                                B        =                                                            -                A                            ⁢                                                ∑                                      i                    =                    0                                                        N                    -                    1                                                  ⁢                                  X                  ⁡                                      (                    i                    )                                                                        +                                          ∑                                  i                  =                  0                                                  N                  -                  1                                            ⁢                              Y                ⁡                                  (                  i                  )                                                              N                                    (        2        )            
Forcing xe2x80x9cAxe2x80x9d to 0 and only calculating xe2x80x9cBxe2x80x9d, then, will give an average of y(i).
In FIG. 1, the horizontal axis represents observation points and the vertical axis represents observed values at those observation points. xe2x80x9cAxe2x80x9d represents the gradient of estimation result 107 and xe2x80x9cBxe2x80x9d represents the intercept.
FIG. 2 is a block diagram showing a configuration of a first embodiment of a receiver which utilizes the conventional least square method as linear predicting. Receiver 200 shown in FIG. 2 is one of examples of general receivers which utilize the conventional least square method to estimate a series of received signals.
Receiver 200 shown in FIG. 2 has observation apparatus 202 and least square prediction apparatus 203. Adder 201, which is not included in receiver 200, is described so as to express that received signals are added by disturbances before input into observation apparatus 202.
In the configuration, a series of signals is input to adder 201, which is supposed to be such a series of signals that can be expressed by a linear expression.
During signal propagation, disturbances like thermal noises are multiplied into a series of signals in adder 201. This result is observed by observation apparatus 202. Since the observation result includes disturbances, the observation result contains a certain margin of error even if the series of signals is one that can be expressed by a linear expression.
Least square prediction apparatus 203 estimates a prediction series by minimizing the square of the margin of error based on the aforementioned expressions (1) and (2). This allows a value close to the series of signals to be obtained as the prediction series even if disturbances exist.
FIG. 3 is a block diagram showing a configuration of a second embodiment of a receiver which utilizes the conventional least square method as linear predicting.
Receiver 300 shown in FIG. 3 has antenna 301, frequency offset compensator 302, demodulator 303, frequency offset detector 304 and least square prediction apparatus 305.
In the configuration, suppose a signal received by antenna 301 already contains disturbances. Frequency offset detector 304 detects frequency offsets from received signals.
Least square prediction apparatus 305 calculates the aforementioned expressions (1) and (2) with the detected frequency offsets to obtain probable frequency offsets, and frequency offset compensator 302 uses this prediction result to compensate received signals. By using the prediction result, demodulator 303 obtains demodulated data of better channel quality with its frequency offset compensated.
FIG. 4 is a block diagram showing a configuration of a third embodiment of a receiver which utilizes the conventional least square method as linear predicting. Receiver 400 shown in FIG. 4 is one of examples of receivers in which the conventional least square method referred to FIG. 2 is applied to phase estimation essential to detecting synchronization. In detecting synchronization, as explained in FIG. 3, even a small margin of error in frequency offset compensation deteriorates the performance, making the reference phase rotate as time goes on. This phase rotation can be expressed in a linear expression using time and phase, so the conventional least square method can be applied to compensate those phase rotation.
Even if frequency offset compensation is completed, to eliminate the influences of constant phase rotation, calculating the averaged phase and compensating the frequency offsets by utilizing the least square method or simply averaging is necessary.
Receiver 400 shown in FIG. 4 has antenna 401, phase compensator 402, demodulator 403, phase error detector 404, and least square prediction apparatus 405.
In the configuration, suppose a signal received by antenna 401 already contains disturbances. Phase compensator 402 compensates the phase shifts of the received signals based on the prediction result obtained from past received signals and demodulator 403 demodulates those result and obtains demodulated data.
Phase error detector 404 detects phase shifts based on the received signals of which phase shifts have been compensated by phase compensator 402.
Least square prediction apparatus 405 calculates the aforementioned expressions (1) and (2) with the detected phase errors to obtain probable frequency offsets, and phase compensator 302 uses this prediction result to compensate received signals. By using the prediction result, demodulator 403 obtains demodulated data of better channel quality with its phase shifts compensated.
FIG. 5 is a block diagram showing a configuration of a fourth embodiment of a receiver which utilizes the conventional least square method as linear predicting. Receiver 500 shown in FIG. 5 is one of examples of receivers in which the conventional least square method referred to FIG. 2 is applied to synchronization shift estimations. Synchronization shifts are originated due to differences in clock oscillation frequencies between transmission and reception. The least square method can be applied to the estimation of synchronization shifts because the relationship between the synchronization and the synchronization shifts can be expressed by a linear expression using time.
Receiver 500 shown in FIG. 5 has antenna 501, synchronization timing adjuster 502, demodulator 503, synchronization shift detector 504 and least square prediction apparatus 505.
In the configuration, suppose a signal received by antenna 501 already contains disturbances. Synchronization shift detector 504 detects the synchronization shifts from the received signals between transmission and reception.
Least square prediction apparatus 505 calculates the aforementioned expressions (1) and (2) with the estimated channel quality, obtains probable synchronization timing, and using this prediction result synchronization shift adjuster 502 corrects the received signals. Using this prediction result, demodulator 503 can obtain high reliability demodulated data with synchronization shifts corrected.
FIG. 6 is a block diagram showing a configuration of a fifth embodiment of a receiver which utilizes the conventional least square method as linear predicting. Receiver 600 shown in FIG. 6 is one of examples of receivers in which the conventional least square method referred to FIG. 2 is applied to channel estimations, presenting an example of diversity combined several reception.
Diversity combination requires to estimate the coefficients weighted to the received signals from each antenna. The least square method can be applied to the estimation of the coefficients for the diversity combining because the quality of the channel can be expressed by a linear expression using time and phase in a short term.
Receiver 600 shown in FIG. 6 has antennas 601 and 602, combiner 603, demodulator 604, channel impulse, response estimators 605 and 606, and least square prediction apparatuses 607 and 608.
In the configuration, suppose a signal received by antennas 601 and 602 already contains disturbances. Channel impulse response estimator 605 estimates channel impulse responses to signals received by antenna 601.
Least square prediction apparatus 607 calculates the aforementioned expressions (1) and (2) using the estimated channel quality, obtains probable channel impulse responses and gives this prediction result to combiner 603 and reflects the result in weighting processing on the signal received by antenna 601.
Channel impulse response estimator 606 estimates channel impulse responses to a signal received by antenna 602. Least square prediction apparatus 608 calculates the aforementioned expressions (1) and (2) using the observed values, obtains probable channel impulse responses and gives this prediction result to combiner 603 and reflects the result in weighting processing on the signal received by antenna 602.
Combiner 603, after the most appropriate coefficients weighted to the signals received by antennas 601 and 602, combines those received signals and obtains high reliability demodulated data through combined diversity.
However, all the receivers above use the conventional least square. method as linear predicting that weights all received signals equally. In the conventional least square method, all observed values influences equally to the linear predicting result. This means that both of the probable observed values, which has high reliability with respect to the predicted values and less probable observed values, which has low reliability due to disturbances or fading, influences equally to the result of linear predicting.
As the lower reliability the observed value has, the longer Euclidean distance becomes, containing low reliability observed values among the observed values is likely to have serious adverse effects on the predicted result using the least square method.
In other words, the least square method is a method to determine the xe2x80x9cgradientxe2x80x9d and xe2x80x9cintercept, xe2x80x9d parameters of a linear expression, that minimizes the Euclidean distance from every observation point. It is capable of predicting best when all the observation points have the same probability, but if the likelihood varies depending on the observation points, it is affected by less likely observed points, deteriorating its prediction accuracy.
For a receiver which utilizes the linear prediction for frequency offset compensation, phase compensation, synchronization shift correction and combined diversity, low accuracy of the linear prediction can result in a problem of not only reducing the accuracy of received signal correction but also increasing the error rate of the channel.
The main point of the present invention is that, it is after weighting every observed value with corresponding likelihood at every observation points that a least square method is utilized as a linear prediction to the observations, in order to improve the accuracy of the linear prediction by making the influence of the probable observed values to the prediction result larger.
The present invention can improve the accuracy of received signal correction in a receiver, by making a linear prediction more reliable, which is applied for frequency offset compensation, phase shift compensation, synchronization shift adjustment, combined diversity and other signal values estimation.